




'''simple docstring'''







import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch

@require_torch
@require_optimum
@slow
class 	_snake_case(			unittest.TestCase      ):






 def 						_UpperCamelCase (self       :  Dict    )       ->   Tuple:






    """simple docstring"""


    A__       =     'hf-internal-testing/tiny-random-t5'
    A__       =     AutoTokenizer.from_pretrained(a    )
    A__       =     AutoModelForSeqaSeqLM.from_pretrained(a    )

    A__       =     tokenizer('This is me'    ,		return_tensors='pt'    )

    A__       =     model.to_bettertransformer()

    self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()    )    )

    A__       =     model.generate(**a    )

    A__       =     model.reverse_bettertransformer()

    self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()    )    )

    with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(a    )

       A__       =     AutoModelForSeqaSeqLM.from_pretrained(a    )

       self.assertFalse(
           any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()    )    )

       A__       =     model_reloaded.generate(**a    )
       self.assertTrue(torch.allclose(a    ,		a    )    )






 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     'hf-internal-testing/tiny-random-t5'
    A__       =     AutoModelForSeqaSeqLM.from_pretrained(a    )

    A__       =     model.to_bettertransformer()

    with tempfile.TemporaryDirectory() as tmpdirname:
       with self.assertRaises(a    ):
          model.save_pretrained(a    )

       A__       =     model.reverse_bettertransformer()
       model.save_pretrained(a    )








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_     						=				{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


